The liver is a large, complex organ that performs diverse functions. Diseases associated with the liver are generally referred to as hepatic diseases. Some types of cancer (e.g., colorectal, breast) spread metastases to the liver. Using conventional systems, metastatic disease may remain subclinical for a lengthy period of time since so much of the liver has to be affected before liver function begins to fail. Hepatic diseases may be diffuse or focal. Diffuse liver disease may include, for example, infection, autoimmune inflammation, fatty infiltration, cirrhosis, or other diseases. Focal liver disease may include, for example, primary liver cancers such as hepatocellular carcinoma, and metastases from various cancers.
Since the liver has different regions and multiple blood supplies, compartmental models have been used to study the liver. A compartmental analysis is a form of deterministic analysis that divides a physiological system into a number of interconnected compartments. A compartment may be an anatomical, physiological, chemical, or physical subdivision of a system. A compartmental model may be characterized by number of compartments, number of inputs, or number of outputs. In a deterministic model, analytical expressions are used to describe behavior. This compares to a stochastic model where behavior is determined by random processes that are described by probability functions. Since the liver has a dual blood supply, the liver may be studied using a dual input, single compartment model, a dual input, dual compartment model (e.g., for tracer kinetics), or using other models. Conventionally, it has been difficult, if even possible at all, to assess hemodynamic changes in the liver due to the dual blood supply.
Non-invasive imaging methods have been employed to detect and characterize liver disease. Non-invasive imaging methods have also been used for evaluating hepatic vascular and segmental anatomy to support, for example, planning surgery. Non-invasive imaging methods have also been used to detect early pathological arterial vascularization to diagnosis hypervascular tumors, including metastases from carcinoid, endocrine tumors, hepatic cell carcinoma, and other cancers. However, conventional imaging systems have been challenged by the requirements for providing imaging over a large volume while simultaneously providing a dynamic ability to detect changes in blood flow and to detect contrast media enhancement over time all while providing clinically relevant temporal and spatial resolution.
Magnetic resonance imaging (MRI) provides highly detailed anatomical information. Dynamic contrast-enhanced (DCE) MRI of the liver monitors the transit of contrast materials (e.g., gadolinium (Gd) chelates) through the liver. Different contrast agents have been employed in liver-based MRI. For example, Gd-DTPA was used as early as 1988. More recently, Gd-BOPTA (gadolinium benzyloxy-propionic tetraacetate or gadobenate dimeglumine) and Gd-EOB-DTPA (gadolinium ethozybenzyl diethylenetriamine-pentaacetic acid) have been used. Gadolinium based contrast agents are typically employed to shorten T1 in regions where the Gd concentrates. Gd-BOPTA is distributed in the body like ordinary extracellular contrast agents (e.g., Gd-DTPA). However, in the liver, Gd-BOPTA is taken up by hepatocytes and is excreted into the biliary canaliculi in an adenosine triphosphate (ATP) dependent process. Hepatocytes are polarized cells that have two functionally distinct sides, including one that faces the blood and extracellular fluids. Gd-BOPTA enhancement may reach a peak 60-120 minutes after contrast agent introduction. Gd-EOB-DTPA combines hepatocellular specificity with T1-relaxivity and extracellular behavior. Gd-EOB-DTPA is first distributed into the extracellular spaces and then taken up by hepatocytes. Gd-EOB-DTPA enhancement may reach a peak in the liver about 20 minutes after contrast agent introduction.
MRI using DCE may appear different depending on how long after contrast administration the images are obtained. For example, a portal venous phase may be experienced starting at approximately 45-50 seconds after contrast agent introduction and an equilibrium or interstitial phase may be experienced after around 120-180 seconds after contrast agent introduction. The images have a different appearance (e.g., different structures have varying levels of brightness) at different time-points after contrast.
Conventionally, different methods have been used to quantify liver perfusion using information acquired by MRI. These methods included the upslope method, semi-quantitative parametric methods, de-convolution methods, and various compartmental models. Unfortunately, the temporal resolution provided by conventional MRI systems may not have been sufficient to support functional examinations. Additionally, applying conventional under-sampling to improve temporal resolution may have negatively impacted spatial resolution to the point where functional examinations were difficult, if even possible at all, to achieve.
Conventional studies have typically employed T1-weighted, gradient recalled echo (GRE) sequences. T1 refers to spin-lattice relaxation, T2 refers to spin-spin relaxation. T1 relaxation is caused by interactions between excited protons and local electromagnetic fields associated with neighboring structure. T2 relaxation depends on the continuous dephasing of precessing protons caused by local magnetic field inhomogeneities. T2 is faster than T1. A GRE sequence applies varying gradient fields to refocus spins. A spin echo (SE) sequence uses RF pulses to refocus spins.
Three-dimensional (3D) acquisitions may provide continuous whole-liver coverage to assess whole-liver perfusion, but have been limited by longer acquisition times. 3D T1 mapping within one breath-hold has typically been challenging given the size of the liver. Thus, two-dimensional (2D) images have typically been acquired with higher temporal and spatial resolution. However, the 2D image approach may have been limited to a single representative slice or selected slices, which precluded whole liver perfusion analysis. Achieving higher temporal and spatial resolution facilitates achieving greater precision in estimating liver perfusion rates.
Kinetic modeling involves converting an MRI signal into a gadolinium (Gd) concentration. This conversion has been challenging because MR signal intensity varies with contrast agent concentration, pulse sequence parameters, pre-contrast relaxation times, blood flow velocity, and other factors. Additionally, the relationship between signal and concentration is non-linear. Conventional spatial and temporal resolution may have been insufficient to provide adequate signal for meaningful functional analysis involving kinetic modeling.
Conventionally, to reduce artifacts associated with subject motion during image acquisition, significant breath holds were required. The breath holds were both long and repeated. A subject who is having their liver imaged may be challenged to hold their breath for a sufficient period of time.